100 citations · 125 across the 16 of their papers we have counts for
11 papers · 1 filter
3D Uncertainty Quantification for Photoacoustic Tomography
Babak Maboudi Afkham, Amal Mohammed A Alghami, Hassan Yazdanian +1
Photoacoustic tomography (PAT) is a promising modality for high-resolution biomedical imaging, motivating the need for reliable uncertainty quantification (UQ) of reconstructed ima…
Adjoint-Based Bayesian Uncertainty Quantification for PDE-Constrained Inverse Problems with Application to Semiconductor Imaging
Hassan Yazdanian, Leila Taghizadeh, Babak Maboudi Afkham
We formulate a Bayesian framework for reconstructing doping profiles in pn-junction semiconductor devices from boundary flux measurements. The unknown doping field is modeled as a…
QVaR: a Quantum Variational Regularization method for Linear Inverse Problems
Siiri Rautio, Hjørdis Schlüter, Andreas Hauptmann +1
We present a tailored framework for solving regularized linear inverse problems using quantum optimization methods. By discretizing the solution space and encoding data fidelity an…
Inhomogeneous Priors for Bayesian Inverse Problems
Babak Maboudi Afkham, Tomas Soto, Mirza Karamehmedovic +1
Many inverse problems arising in engineering and applied sciences involve unknown quantities with pronounced spatial inhomogeneity, such as localized defects or spatially varying m…
CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python
Amal M A Alghamdi, Nicolai A B Riis, Babak M Afkham +4
Inverse problems, particularly those governed by Partial Differential Equations (PDEs), are prevalent in various scientific and engineering applications, and uncertainty quantifica…
CUQIpy: I. Computational uncertainty quantification for inverse problems in Python
Nicolai A B Riis, Amal M A Alghamdi, Felipe Uribe +4
This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series…
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient goal-oriented uncertainty quantification for inverse problems. Contrary…
Uncertainty Quantification of Inclusion Boundaries in the Context of X-ray Tomography
Babak Maboudi Afkham, Yiqiu Dong, Per Christian Hansen
In this work, we describe a Bayesian framework for reconstructing the boundaries of piecewise smooth regions in the X-ray computed tomography (CT) problem in an infinite-dimensiona…
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning ap…
Symplectic Model-Reduction with a Weighted Inner Product
Babak Maboudi Afkham, Ashish Bhatt, Bernard Haasdonk +1
In the recent years, considerable attention has been paid to preserving structures and invariants in reduced basis methods, in order to enhance the stability and robustness of the…
Structure Preserving Model Reduction of Parametric Hamiltonian Systems
Babak Maboudi Afkham, Jan S. Hesthaven
While reduced-order models (ROMs) have been popular for efficiently solving large systems of differential equations, the stability of reduced models over long-time integration is o…